code: def __init__(
      self,
      out_layers,
      r_channels,
      channels=[128, 128],
      upscale_factors=[16, 16],
  ):
      super(UpsampleNet, self).__init__()
      for channel, factor in zip(channels, upscale_factors):
          self.add_link(
              L.Deconvolution1D(
                  None, channel, factor, stride=factor, pad=0
              )
          )
      for i in range(out_layers):
          self.add_link(L.Convolution1D(None, 2 * r_channels, 1))
      self.n_deconvolutions = len(channels)
  
file_path: chainer/examples/wavenet/net.py
imports: import chainer
  import chainer.functions
  import chainer.links
  from modules import ResidualNet
label_desc: Use image node_id 1 to create a new UpsampleNet object from inherited base classes: chainer with example: obj = UpsampleNet(out_layers, r_channels, channels, upscale_factors)